<<<<<<< HEAD Pandas Profiling Report

Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations10841
Missing cells1486
Missing cells (%)1.1%
Duplicate rows410
Duplicate rows (%)3.8%
Total size in memory1.1 MiB
Average record size in memory104.0 B

Variable types

Text5
Categorical5
Numeric2
DateTime1

Alerts

Dataset has 410 (3.8%) duplicate rowsDuplicates
Type is highly imbalanced (62.0%) Imbalance
Content Rating is highly imbalanced (61.5%) Imbalance
Rating has 1474 (13.6%) missing values Missing
Reviews has 596 (5.5%) zeros Zeros

Reproduction

Analysis started2025-02-04 20:58:06.107438
Analysis finished2025-02-04 20:58:14.032345
Duration7.92 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

App
Text

Distinct9660
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
2025-02-05T01:58:15.124426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length194
Median length75
Mean length22.517111
Min length1

Characters and Unicode

Total characters244108
Distinct characters478
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8862 ?
Unique (%)81.7%

Sample

1st rowPhoto Editor & Candy Camera & Grid & ScrapBook
2nd rowColoring book moana
3rd rowU Launcher Lite – FREE Live Cool Themes, Hide Apps
4th rowSketch - Draw & Paint
5th rowPixel Draw - Number Art Coloring Book
ValueCountFrequency (%)
2821
 
6.6%
for 560
 
1.3%
free 513
 
1.2%
app 332
 
0.8%
and 283
 
0.7%
the 270
 
0.6%
mobile 222
 
0.5%
news 195
 
0.5%
live 194
 
0.5%
video 194
 
0.5%
Other values (9549) 36995
86.9%
2025-02-05T01:58:17.341347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
31738
 
13.0%
e 19421
 
8.0%
a 14838
 
6.1%
o 13637
 
5.6%
r 13242
 
5.4%
i 12140
 
5.0%
t 10335
 
4.2%
n 9847
 
4.0%
s 8715
 
3.6%
l 8546
 
3.5%
Other values (468) 101649
41.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 244108
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
31738
 
13.0%
e 19421
 
8.0%
a 14838
 
6.1%
o 13637
 
5.6%
r 13242
 
5.4%
i 12140
 
5.0%
t 10335
 
4.2%
n 9847
 
4.0%
s 8715
 
3.6%
l 8546
 
3.5%
Other values (468) 101649
41.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 244108
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
31738
 
13.0%
e 19421
 
8.0%
a 14838
 
6.1%
o 13637
 
5.6%
r 13242
 
5.4%
i 12140
 
5.0%
t 10335
 
4.2%
n 9847
 
4.0%
s 8715
 
3.6%
l 8546
 
3.5%
Other values (468) 101649
41.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 244108
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
31738
 
13.0%
e 19421
 
8.0%
a 14838
 
6.1%
o 13637
 
5.6%
r 13242
 
5.4%
i 12140
 
5.0%
t 10335
 
4.2%
n 9847
 
4.0%
s 8715
 
3.6%
l 8546
 
3.5%
Other values (468) 101649
41.6%

Category
Categorical

Distinct33
Distinct (%)0.3%
Missing1
Missing (%)< 0.1%
Memory size84.8 KiB
FAMILY
1972 
GAME
1144 
TOOLS
843 
MEDICAL
 
463
BUSINESS
 
460
Other values (28)
5958 

Length

Max length19
Median length16
Mean length9.0244465
Min length4

Characters and Unicode

Total characters97825
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowART_AND_DESIGN
2nd rowART_AND_DESIGN
3rd rowART_AND_DESIGN
4th rowART_AND_DESIGN
5th rowART_AND_DESIGN

Common Values

ValueCountFrequency (%)
FAMILY 1972
18.2%
GAME 1144
 
10.6%
TOOLS 843
 
7.8%
MEDICAL 463
 
4.3%
BUSINESS 460
 
4.2%
PRODUCTIVITY 424
 
3.9%
PERSONALIZATION 392
 
3.6%
COMMUNICATION 387
 
3.6%
SPORTS 384
 
3.5%
LIFESTYLE 382
 
3.5%
Other values (23) 3989
36.8%

Length

2025-02-05T01:58:17.852976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
family 1972
18.2%
game 1144
 
10.6%
tools 843
 
7.8%
medical 463
 
4.3%
business 460
 
4.2%
productivity 424
 
3.9%
personalization 392
 
3.6%
communication 387
 
3.6%
sports 384
 
3.5%
lifestyle 382
 
3.5%
Other values (23) 3989
36.8%

Most occurring characters

ValueCountFrequency (%)
A 10424
 
10.7%
I 8783
 
9.0%
E 7958
 
8.1%
N 7335
 
7.5%
O 7125
 
7.3%
S 6558
 
6.7%
L 6189
 
6.3%
T 5894
 
6.0%
M 5155
 
5.3%
_ 3575
 
3.7%
Other values (14) 28829
29.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 97825
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 10424
 
10.7%
I 8783
 
9.0%
E 7958
 
8.1%
N 7335
 
7.5%
O 7125
 
7.3%
S 6558
 
6.7%
L 6189
 
6.3%
T 5894
 
6.0%
M 5155
 
5.3%
_ 3575
 
3.7%
Other values (14) 28829
29.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 97825
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 10424
 
10.7%
I 8783
 
9.0%
E 7958
 
8.1%
N 7335
 
7.5%
O 7125
 
7.3%
S 6558
 
6.7%
L 6189
 
6.3%
T 5894
 
6.0%
M 5155
 
5.3%
_ 3575
 
3.7%
Other values (14) 28829
29.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 97825
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 10424
 
10.7%
I 8783
 
9.0%
E 7958
 
8.1%
N 7335
 
7.5%
O 7125
 
7.3%
S 6558
 
6.7%
L 6189
 
6.3%
T 5894
 
6.0%
M 5155
 
5.3%
_ 3575
 
3.7%
Other values (14) 28829
29.5%

Rating
Real number (ℝ)

Missing 

Distinct39
Distinct (%)0.4%
Missing1474
Missing (%)13.6%
Infinite0
Infinite (%)0.0%
Mean4.1915128
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size84.8 KiB
2025-02-05T01:58:18.180101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.2
Q14
median4.3
Q34.5
95-th percentile4.8
Maximum5
Range4
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.51573524
Coefficient of variation (CV)0.12304275
Kurtosis5.7889866
Mean4.1915128
Median Absolute Deviation (MAD)0.2
Skewness-1.8518947
Sum39261.9
Variance0.26598284
MonotonicityNot monotonic
2025-02-05T01:58:18.545122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
4.4 1109
10.2%
4.3 1076
9.9%
4.5 1038
9.6%
4.2 952
8.8%
4.6 823
7.6%
4.1 708
 
6.5%
4 568
 
5.2%
4.7 499
 
4.6%
3.9 386
 
3.6%
3.8 303
 
2.8%
Other values (29) 1905
17.6%
(Missing) 1474
13.6%
ValueCountFrequency (%)
1 16
0.1%
1.2 1
 
< 0.1%
1.4 3
 
< 0.1%
1.5 3
 
< 0.1%
1.6 4
 
< 0.1%
1.7 8
0.1%
1.8 8
0.1%
1.9 14
0.1%
2 12
0.1%
2.1 8
0.1%
ValueCountFrequency (%)
5 274
 
2.5%
4.9 87
 
0.8%
4.8 234
 
2.2%
4.7 499
4.6%
4.6 823
7.6%
4.5 1038
9.6%
4.4 1109
10.2%
4.3 1076
9.9%
4.2 952
8.8%
4.1 708
6.5%

Reviews
Real number (ℝ)

Zeros 

Distinct6001
Distinct (%)55.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean444111.93
Minimum0
Maximum78158306
Zeros596
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size84.8 KiB
2025-02-05T01:58:18.887207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q138
median2094
Q354768
95-th percentile1461698
Maximum78158306
Range78158306
Interquartile range (IQR)54730

Descriptive statistics

Standard deviation2927628.7
Coefficient of variation (CV)6.5920964
Kurtosis341.09154
Mean444111.93
Median Absolute Deviation (MAD)2094
Skewness16.450332
Sum4.8146174 × 109
Variance8.5710096 × 1012
MonotonicityNot monotonic
2025-02-05T01:58:19.256221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 596
 
5.5%
1 272
 
2.5%
2 214
 
2.0%
3 175
 
1.6%
4 137
 
1.3%
5 108
 
1.0%
6 97
 
0.9%
7 90
 
0.8%
8 74
 
0.7%
9 65
 
0.6%
Other values (5991) 9013
83.1%
ValueCountFrequency (%)
0 596
5.5%
1 272
2.5%
2 214
 
2.0%
3 175
 
1.6%
4 137
 
1.3%
5 108
 
1.0%
6 97
 
0.9%
7 90
 
0.8%
8 74
 
0.7%
9 65
 
0.6%
ValueCountFrequency (%)
78158306 1
< 0.1%
78128208 1
< 0.1%
69119316 2
< 0.1%
69109672 1
< 0.1%
66577446 1
< 0.1%
66577313 2
< 0.1%
66509917 1
< 0.1%
56646578 1
< 0.1%
56642847 2
< 0.1%
44893888 1
< 0.1%

Size
Text

Distinct461
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
2025-02-05T01:58:20.528314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length5
Mean length5.7052855
Min length3

Characters and Unicode

Total characters61851
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique245 ?
Unique (%)2.3%

Sample

1st row19M
2nd row14M
3rd row8.7M
4th row25M
5th row2.8M
ValueCountFrequency (%)
varies 1695
 
11.9%
with 1695
 
11.9%
device 1695
 
11.9%
11m 198
 
1.4%
12m 196
 
1.4%
14m 194
 
1.4%
13m 191
 
1.3%
15m 184
 
1.3%
17m 160
 
1.1%
19m 154
 
1.1%
Other values (453) 7869
55.3%
2025-02-05T01:58:22.010871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 8830
 
14.3%
e 5085
 
8.2%
i 5085
 
8.2%
. 3610
 
5.8%
3390
 
5.5%
1 3095
 
5.0%
2 2791
 
4.5%
3 2343
 
3.8%
4 1966
 
3.2%
5 1741
 
2.8%
Other values (16) 23915
38.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61851
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 8830
 
14.3%
e 5085
 
8.2%
i 5085
 
8.2%
. 3610
 
5.8%
3390
 
5.5%
1 3095
 
5.0%
2 2791
 
4.5%
3 2343
 
3.8%
4 1966
 
3.2%
5 1741
 
2.8%
Other values (16) 23915
38.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61851
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 8830
 
14.3%
e 5085
 
8.2%
i 5085
 
8.2%
. 3610
 
5.8%
3390
 
5.5%
1 3095
 
5.0%
2 2791
 
4.5%
3 2343
 
3.8%
4 1966
 
3.2%
5 1741
 
2.8%
Other values (16) 23915
38.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61851
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 8830
 
14.3%
e 5085
 
8.2%
i 5085
 
8.2%
. 3610
 
5.8%
3390
 
5.5%
1 3095
 
5.0%
2 2791
 
4.5%
3 2343
 
3.8%
4 1966
 
3.2%
5 1741
 
2.8%
Other values (16) 23915
38.7%

Installs
Categorical

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
1,000,000+
1579 
10,000,000+
1252 
100,000+
1169 
10,000+
1054 
1,000+
908 
Other values (16)
4879 

Length

Max length14
Median length11
Mean length7.9186422
Min length1

Characters and Unicode

Total characters85846
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row10,000+
2nd row500,000+
3rd row5,000,000+
4th row50,000,000+
5th row100,000+

Common Values

ValueCountFrequency (%)
1,000,000+ 1579
14.6%
10,000,000+ 1252
11.5%
100,000+ 1169
10.8%
10,000+ 1054
9.7%
1,000+ 908
8.4%
5,000,000+ 752
 
6.9%
100+ 719
 
6.6%
500,000+ 539
 
5.0%
50,000+ 479
 
4.4%
5,000+ 477
 
4.4%
Other values (11) 1913
17.6%

Length

2025-02-05T01:58:22.275166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,000,000 1579
14.6%
10,000,000 1252
11.5%
100,000 1169
10.8%
10,000 1054
9.7%
1,000 908
8.4%
5,000,000 752
 
6.9%
100 719
 
6.6%
500,000 539
 
5.0%
50,000 479
 
4.4%
5,000 477
 
4.4%
Other values (10) 1913
17.6%

Most occurring characters

ValueCountFrequency (%)
0 50674
59.0%
, 13506
 
15.7%
+ 10840
 
12.6%
1 7601
 
8.9%
5 3225
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 85846
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 50674
59.0%
, 13506
 
15.7%
+ 10840
 
12.6%
1 7601
 
8.9%
5 3225
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 85846
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 50674
59.0%
, 13506
 
15.7%
+ 10840
 
12.6%
1 7601
 
8.9%
5 3225
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 85846
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 50674
59.0%
, 13506
 
15.7%
+ 10840
 
12.6%
1 7601
 
8.9%
5 3225
 
3.8%

Type
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size84.8 KiB
Free
10040 
Paid
 
800

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters43360
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFree
2nd rowFree
3rd rowFree
4th rowFree
5th rowFree

Common Values

ValueCountFrequency (%)
Free 10040
92.6%
Paid 800
 
7.4%
(Missing) 1
 
< 0.1%

Length

2025-02-05T01:58:22.555414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-05T01:58:22.711277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
free 10040
92.6%
paid 800
 
7.4%

Most occurring characters

ValueCountFrequency (%)
e 20080
46.3%
F 10040
23.2%
r 10040
23.2%
P 800
 
1.8%
a 800
 
1.8%
i 800
 
1.8%
d 800
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 43360
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 20080
46.3%
F 10040
23.2%
r 10040
23.2%
P 800
 
1.8%
a 800
 
1.8%
i 800
 
1.8%
d 800
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 43360
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 20080
46.3%
F 10040
23.2%
r 10040
23.2%
P 800
 
1.8%
a 800
 
1.8%
i 800
 
1.8%
d 800
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 43360
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 20080
46.3%
F 10040
23.2%
r 10040
23.2%
P 800
 
1.8%
a 800
 
1.8%
i 800
 
1.8%
d 800
 
1.8%

Price
Text

Distinct92
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
2025-02-05T01:58:23.286360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length1
Mean length1.3793008
Min length1

Characters and Unicode

Total characters14953
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)0.5%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 10041
92.6%
0.99 148
 
1.4%
2.99 129
 
1.2%
1.99 73
 
0.7%
4.99 72
 
0.7%
3.99 63
 
0.6%
1.49 46
 
0.4%
5.99 30
 
0.3%
2.49 26
 
0.2%
9.99 21
 
0.2%
Other values (82) 192
 
1.8%
2025-02-05T01:58:24.153844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 10246
68.5%
9 1477
 
9.9%
$ 800
 
5.4%
. 800
 
5.4%
800
 
5.4%
4 220
 
1.5%
2 193
 
1.3%
1 189
 
1.3%
3 104
 
0.7%
5 52
 
0.3%
Other values (3) 72
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14953
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 10246
68.5%
9 1477
 
9.9%
$ 800
 
5.4%
. 800
 
5.4%
800
 
5.4%
4 220
 
1.5%
2 193
 
1.3%
1 189
 
1.3%
3 104
 
0.7%
5 52
 
0.3%
Other values (3) 72
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14953
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 10246
68.5%
9 1477
 
9.9%
$ 800
 
5.4%
. 800
 
5.4%
800
 
5.4%
4 220
 
1.5%
2 193
 
1.3%
1 189
 
1.3%
3 104
 
0.7%
5 52
 
0.3%
Other values (3) 72
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14953
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 10246
68.5%
9 1477
 
9.9%
$ 800
 
5.4%
. 800
 
5.4%
800
 
5.4%
4 220
 
1.5%
2 193
 
1.3%
1 189
 
1.3%
3 104
 
0.7%
5 52
 
0.3%
Other values (3) 72
 
0.5%

Content Rating
Categorical

Imbalance 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
Everyone
8715 
Teen
1208 
Mature 17+
 
499
Everyone 10+
 
414
Adults only 18+
 
3

Length

Max length15
Median length8
Mean length7.8008486
Min length4

Characters and Unicode

Total characters84569
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEveryone
2nd rowEveryone
3rd rowEveryone
4th rowTeen
5th rowEveryone

Common Values

ValueCountFrequency (%)
Everyone 8715
80.4%
Teen 1208
 
11.1%
Mature 17+ 499
 
4.6%
Everyone 10+ 414
 
3.8%
Adults only 18+ 3
 
< 0.1%
Unrated 2
 
< 0.1%

Length

2025-02-05T01:58:24.418138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-05T01:58:24.630568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
everyone 9129
77.6%
teen 1208
 
10.3%
mature 499
 
4.2%
17 499
 
4.2%
10 414
 
3.5%
adults 3
 
< 0.1%
only 3
 
< 0.1%
18 3
 
< 0.1%
unrated 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 21175
25.0%
n 10342
12.2%
r 9630
11.4%
y 9132
10.8%
o 9132
10.8%
v 9129
10.8%
E 9129
10.8%
T 1208
 
1.4%
919
 
1.1%
1 916
 
1.1%
Other values (13) 3857
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84569
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 21175
25.0%
n 10342
12.2%
r 9630
11.4%
y 9132
10.8%
o 9132
10.8%
v 9129
10.8%
E 9129
10.8%
T 1208
 
1.4%
919
 
1.1%
1 916
 
1.1%
Other values (13) 3857
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84569
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 21175
25.0%
n 10342
12.2%
r 9630
11.4%
y 9132
10.8%
o 9132
10.8%
v 9129
10.8%
E 9129
10.8%
T 1208
 
1.4%
919
 
1.1%
1 916
 
1.1%
Other values (13) 3857
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84569
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 21175
25.0%
n 10342
12.2%
r 9630
11.4%
y 9132
10.8%
o 9132
10.8%
v 9129
10.8%
E 9129
10.8%
T 1208
 
1.4%
919
 
1.1%
1 916
 
1.1%
Other values (13) 3857
 
4.6%

Genres
Text

Distinct120
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
2025-02-05T01:58:25.390012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length37
Median length32
Mean length10.421917
Min length4

Characters and Unicode

Total characters112984
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)0.2%

Sample

1st rowArt & Design
2nd rowArt & Design;Pretend Play
3rd rowArt & Design
4th rowArt & Design
5th rowArt & Design;Creativity
ValueCountFrequency (%)
2073
 
13.4%
tools 842
 
5.5%
entertainment 623
 
4.0%
education 549
 
3.6%
medical 463
 
3.0%
business 460
 
3.0%
productivity 424
 
2.7%
sports 398
 
2.6%
personalization 392
 
2.5%
communication 387
 
2.5%
Other values (126) 8837
57.2%
2025-02-05T01:58:26.504744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 9816
 
8.7%
o 9089
 
8.0%
e 8940
 
7.9%
n 8905
 
7.9%
t 8623
 
7.6%
a 8618
 
7.6%
s 6282
 
5.6%
4607
 
4.1%
l 4601
 
4.1%
r 4532
 
4.0%
Other values (36) 38971
34.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112984
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 9816
 
8.7%
o 9089
 
8.0%
e 8940
 
7.9%
n 8905
 
7.9%
t 8623
 
7.6%
a 8618
 
7.6%
s 6282
 
5.6%
4607
 
4.1%
l 4601
 
4.1%
r 4532
 
4.0%
Other values (36) 38971
34.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112984
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 9816
 
8.7%
o 9089
 
8.0%
e 8940
 
7.9%
n 8905
 
7.9%
t 8623
 
7.6%
a 8618
 
7.6%
s 6282
 
5.6%
4607
 
4.1%
l 4601
 
4.1%
r 4532
 
4.0%
Other values (36) 38971
34.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112984
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 9816
 
8.7%
o 9089
 
8.0%
e 8940
 
7.9%
n 8905
 
7.9%
t 8623
 
7.6%
a 8618
 
7.6%
s 6282
 
5.6%
4607
 
4.1%
l 4601
 
4.1%
r 4532
 
4.0%
Other values (36) 38971
34.5%
Distinct1377
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
Minimum2010-05-21 00:00:00
Maximum2018-08-08 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-05T01:58:26.817904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T01:58:27.230801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2783
Distinct (%)25.7%
Missing8
Missing (%)0.1%
Memory size84.8 KiB
2025-02-05T01:58:28.692417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length50
Median length39
Mean length6.6437737
Min length1

Characters and Unicode

Total characters71972
Distinct characters79
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1784 ?
Unique (%)16.5%

Sample

1st row1.0.0
2nd row2.0.0
3rd row1.2.4
4th rowVaries with device
5th row1.1
ValueCountFrequency (%)
varies 1459
 
10.5%
with 1459
 
10.5%
device 1459
 
10.5%
1 844
 
6.1%
1.1 278
 
2.0%
1.2 185
 
1.3%
2 166
 
1.2%
1.3 147
 
1.1%
1.0.0 136
 
1.0%
1.0.1 120
 
0.9%
Other values (2824) 7674
55.1%
2025-02-05T01:58:30.680286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 14308
19.9%
1 8918
12.4%
0 4540
 
6.3%
e 4508
 
6.3%
i 4464
 
6.2%
2 4316
 
6.0%
3094
 
4.3%
3 2771
 
3.9%
4 2164
 
3.0%
5 1729
 
2.4%
Other values (69) 21160
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 71972
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 14308
19.9%
1 8918
12.4%
0 4540
 
6.3%
e 4508
 
6.3%
i 4464
 
6.2%
2 4316
 
6.0%
3094
 
4.3%
3 2771
 
3.9%
4 2164
 
3.0%
5 1729
 
2.4%
Other values (69) 21160
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 71972
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 14308
19.9%
1 8918
12.4%
0 4540
 
6.3%
e 4508
 
6.3%
i 4464
 
6.2%
2 4316
 
6.0%
3094
 
4.3%
3 2771
 
3.9%
4 2164
 
3.0%
5 1729
 
2.4%
Other values (69) 21160
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 71972
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 14308
19.9%
1 8918
12.4%
0 4540
 
6.3%
e 4508
 
6.3%
i 4464
 
6.2%
2 4316
 
6.0%
3094
 
4.3%
3 2771
 
3.9%
4 2164
 
3.0%
5 1729
 
2.4%
Other values (69) 21160
29.4%

Android Ver
Categorical

Distinct33
Distinct (%)0.3%
Missing2
Missing (%)< 0.1%
Memory size84.8 KiB
4.1 and up
2451 
4.0.3 and up
1501 
4.0 and up
1376 
Varies with device
1362 
4.4 and up
980 
Other values (28)
3169 

Length

Max length18
Median length10
Mean length11.337116
Min length9

Characters and Unicode

Total characters122883
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row4.0.3 and up
2nd row4.0.3 and up
3rd row4.0.3 and up
4th row4.2 and up
5th row4.4 and up

Common Values

ValueCountFrequency (%)
4.1 and up 2451
22.6%
4.0.3 and up 1501
13.8%
4.0 and up 1376
12.7%
Varies with device 1362
12.6%
4.4 and up 980
 
9.0%
2.3 and up 652
 
6.0%
5.0 and up 601
 
5.5%
4.2 and up 394
 
3.6%
2.3.3 and up 281
 
2.6%
2.2 and up 244
 
2.3%
Other values (23) 997
9.2%

Length

2025-02-05T01:58:30.964526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and 9468
29.1%
up 9468
29.1%
4.1 2452
 
7.5%
4.0.3 1503
 
4.6%
4.0 1376
 
4.2%
varies 1362
 
4.2%
with 1362
 
4.2%
device 1362
 
4.2%
4.4 980
 
3.0%
2.3 652
 
2.0%
Other values (22) 2532
 
7.8%

Most occurring characters

ValueCountFrequency (%)
21678
17.6%
. 11283
9.2%
d 10830
8.8%
a 10830
8.8%
p 9468
 
7.7%
u 9468
 
7.7%
n 9468
 
7.7%
4 7952
 
6.5%
e 4086
 
3.3%
i 4086
 
3.3%
Other values (18) 23734
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 122883
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
21678
17.6%
. 11283
9.2%
d 10830
8.8%
a 10830
8.8%
p 9468
 
7.7%
u 9468
 
7.7%
n 9468
 
7.7%
4 7952
 
6.5%
e 4086
 
3.3%
i 4086
 
3.3%
Other values (18) 23734
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 122883
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
21678
17.6%
. 11283
9.2%
d 10830
8.8%
a 10830
8.8%
p 9468
 
7.7%
u 9468
 
7.7%
n 9468
 
7.7%
4 7952
 
6.5%
e 4086
 
3.3%
i 4086
 
3.3%
Other values (18) 23734
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 122883
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
21678
17.6%
. 11283
9.2%
d 10830
8.8%
a 10830
8.8%
p 9468
 
7.7%
u 9468
 
7.7%
n 9468
 
7.7%
4 7952
 
6.5%
e 4086
 
3.3%
i 4086
 
3.3%
Other values (18) 23734
19.3%

Interactions

2025-02-05T01:58:12.070680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T01:58:11.275170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T01:58:12.367886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T01:58:11.734578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-05T01:58:31.175961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Android VerCategoryContent RatingInstallsRatingReviewsType
Android Ver1.0000.0900.0510.0990.0700.0000.187
Category0.0901.0000.3470.1120.0850.0720.198
Content Rating0.0510.3471.0000.0860.0410.0620.048
Installs0.0990.1120.0861.0000.1760.3120.265
Rating0.0700.0850.0410.1761.0000.1570.071
Reviews0.0000.0720.0620.3120.1571.0000.017
Type0.1870.1980.0480.2650.0710.0171.000

Missing values

2025-02-05T01:58:12.806710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-05T01:58:13.234568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-05T01:58:13.775133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

AppCategoryRatingReviewsSizeInstallsTypePriceContent RatingGenresLast UpdatedCurrent VerAndroid Ver
0Photo Editor & Candy Camera & Grid & ScrapBookART_AND_DESIGN4.115919M10,000+Free0EveryoneArt & Design7-Jan-181.0.04.0.3 and up
1Coloring book moanaART_AND_DESIGN3.996714M500,000+Free0EveryoneArt & Design;Pretend Play15-Jan-182.0.04.0.3 and up
2U Launcher Lite – FREE Live Cool Themes, Hide AppsART_AND_DESIGN4.7875108.7M5,000,000+Free0EveryoneArt & Design1-Aug-181.2.44.0.3 and up
3Sketch - Draw & PaintART_AND_DESIGN4.521564425M50,000,000+Free0TeenArt & Design8-Jun-18Varies with device4.2 and up
4Pixel Draw - Number Art Coloring BookART_AND_DESIGN4.39672.8M100,000+Free0EveryoneArt & Design;Creativity20-Jun-181.14.4 and up
5Paper flowers instructionsART_AND_DESIGN4.41675.6M50,000+Free0EveryoneArt & Design26-Mar-1712.3 and up
6Smoke Effect Photo Maker - Smoke EditorART_AND_DESIGN3.817819M50,000+Free0EveryoneArt & Design26-Apr-181.14.0.3 and up
7Infinite PainterART_AND_DESIGN4.13681529M1,000,000+Free0EveryoneArt & Design14-Jun-186.1.61.14.2 and up
8Garden Coloring BookART_AND_DESIGN4.41379133M1,000,000+Free0EveryoneArt & Design20-Sep-172.9.23.0 and up
9Kids Paint Free - Drawing FunART_AND_DESIGN4.71213.1M10,000+Free0EveryoneArt & Design;Creativity3-Jul-182.84.0.3 and up
AppCategoryRatingReviewsSizeInstallsTypePriceContent RatingGenresLast UpdatedCurrent VerAndroid Ver
10831payermonstationnement.frMAPS_AND_NAVIGATIONNaN389.8M5,000+Free0EveryoneMaps & Navigation13-Jun-182.0.148.04.0 and up
10832FR TidesWEATHER3.81195582k100,000+Free0EveryoneWeather16-Feb-1462.1 and up
10833Chemin (fr)BOOKS_AND_REFERENCE4.844619k1,000+Free0EveryoneBooks & Reference23-Mar-140.82.2 and up
10834FR CalculatorFAMILY4.072.6M500+Free0EveryoneEducation18-Jun-171.0.04.1 and up
10835FR FormsBUSINESSNaN09.6M10+Free0EveryoneBusiness29-Sep-161.1.54.0 and up
10836Sya9a Maroc - FRFAMILY4.53853M5,000+Free0EveryoneEducation25-Jul-171.484.1 and up
10837Fr. Mike Schmitz Audio TeachingsFAMILY5.043.6M100+Free0EveryoneEducation6-Jul-1814.1 and up
10838Parkinson Exercices FRMEDICALNaN39.5M1,000+Free0EveryoneMedical20-Jan-1712.2 and up
10839The SCP Foundation DB fr nn5nBOOKS_AND_REFERENCE4.5114Varies with device1,000+Free0Mature 17+Books & Reference19-Jan-15Varies with deviceVaries with device
10840iHoroscope - 2018 Daily Horoscope & AstrologyLIFESTYLE4.539830719M10,000,000+Free0EveryoneLifestyle25-Jul-18Varies with deviceVaries with device

Duplicate rows

Most frequently occurring

AppCategoryRatingReviewsSizeInstallsTypePriceContent RatingGenresLast UpdatedCurrent VerAndroid Ver# duplicates
59CBS Sports App - Scores, News, Stats & Watch LiveSPORTS4.391031Varies with device5,000,000+Free0EveryoneSports4-Aug-18Varies with device5.0 and up4
159Google KeepPRODUCTIVITY4.4691474Varies with device100,000,000+Free0EveryoneProductivity6-Aug-18Varies with deviceVaries with device4
253NickENTERTAINMENT4.212327925M10,000,000+Free0Everyone 10+Entertainment;Music & Video24-Jan-182.0.84.4 and up4
285Quizlet: Learn Languages & Vocab with FlashcardsEDUCATION4.6211856Varies with device10,000,000+Free0EveryoneEducation1-Aug-18Varies with deviceVaries with device4
316SkyscannerTRAVEL_AND_LOCAL4.548154629M10,000,000+Free0EveryoneTravel & Local6-Aug-185.484.4 and up4
372WatchESPNSPORTS4.12888096.6M10,000,000+Free0EveryoneSports27-Sep-172.5.14.4 and up4
395eBay: Buy & Sell this Summer - Discover Deals Now!SHOPPING4.42788923Varies with device100,000,000+Free0TeenShopping30-Jul-18Varies with deviceVaries with device4
7A&E - Watch Full Episodes of TV ShowsENTERTAINMENT4.02970619M1,000,000+Free0TeenEntertainment16-Jul-183.1.44.4 and up3
14Adult Dirty EmojisDATING2.8805.5M10,000+Free0TeenDating6-Nov-1714.0.3 and up3
28BBC NewsNEWS_AND_MAGAZINES4.3296781Varies with device10,000,000+Free0Everyone 10+News & Magazines24-Jul-18Varies with deviceVaries with device3